code: def forward(
      self,
      x: Tensor,
      edge_index: Tensor,
      batch: Optional[Tensor] = None,
  ) -> Tensor:
      for conv in self.convs:
          x = conv(x, edge_index).relu()
  
      x = global_mean_pool(x, batch)
  
      x = self.lin1(x).relu()
      x = F.dropout(x, p=0.5, training=self.training)
      x = self.lin2(x)
  
      return x
  
file_path: pytorch_geometric/examples/cpp/save_model.py
imports: from typing import Optional
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import BatchNorm1d, Linear, ReLU, Sequential
  from torch_geometric.nn import GINConv, global_mean_pool
label_desc: Use image node_id 2 for calling the GIN obj's underlying member method code with example usage: obj.forward(x, edge_index, batch) and returns: x